Symbol sequence estimation in speech

ABSTRACT

Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.

BACKGROUND Technical Field

The present invention relates to estimation of symbol sequence inspeech.

Description of the Related Art

A speech-recognition system generates text from audio data, such asrecorded verbal conversation, in a process called speech-to-text.Searching for symbol sequences from verbal conversation is important forutilizing the text generated from a speech-to-text process, referred toas speech-to-text data. Existing symbol sequence search techniques aredisclosed in US Patent Publication 2008/0221882A1, US Patent Publication2014/0222419A1, and US Patent Publication 2011/0046953A1. However, therestill remain difficulties in distinguishing one type of symbol sequence(e.g., phone numbers) from other types of symbol sequences (e.g.,customer IDs) in speech-to-text data.

SUMMARY

According to a first aspect of the present invention, provided is acomputer-implemented method including detecting one or more candidatesof a target symbol sequence from a speech-to-text data, extracting arelated portion of each candidate from the speech-to-text data,detecting repetition of at least a partial sequence of each candidatewithin the related portion of the corresponding candidate, labeling thedetected repetition with a repetition indication, and estimating whethereach candidate is the target symbol sequence, using the correspondingrelated portion including the repetition indication of each of thecandidates. According to the first aspect, the method can enableaccurate identification of a target symbol sequence with lesscomputational resources by utilizing an indication of repetition.

According to a second aspect of the present invention, optionallyprovided is the method of the first aspect, where the detecting the oneor more candidates of the target symbol sequence from the speech-to-textdata includes extracting two or more symbol sequences that constituteeach of the candidates, from the speech-to-text data, where the two ormore symbol sequences are separate from each other in the speech-to-textdata. According to the second aspect, the method can enable detection ofcandidates from distant locations in the speech-to-text data.

According to a third aspect of the present invention, optionallyprovided is the method of the second aspect, where detecting repetitionof at least a partial sequence of each candidate within the relatedportion of the corresponding candidate includes detecting at least oneof the two or more symbol sequences that constitute the correspondingcandidate within the related portion of the corresponding candidates.According to the third aspect, the method can enable detection of arepetition corresponding to the symbol sequence, thereby improving theaccuracy of estimation.

According to a fourth aspect of the present invention, optionallyprovided is the method of the second aspect where the extracting two ormore symbol sequences are performed by extracting a predetermined numberof symbol sequences, the two or more symbol sequences do not overlap,and the concatenation of the two or more symbol sequences forms each ofthe candidates. According to the fourth aspect, the method can enabledetection of candidates from distant locations in the speech-to-textdata.

According to a fifth aspect of the present invention, optionallyprovided is the method of the fourth aspect, where the related portionof each of the candidates includes a portion adjacent to the each of thecandidates. According to the fifth aspect, the method can enabledetection of a repetition corresponding to the symbol sequence, therebyimproving the accuracy of estimation.

According to a sixth aspect of the present invention, optionallyprovided is the method of the fifth aspect, where the estimating whethereach candidate is the target symbol sequence, based on the repetitionindication of each corresponding candidate includes estimating aprobability that each candidate is the target symbol sequence byinputting the related portion of each candidate with the repetitionindication into a recurrent neural network. According to the sixthaspect, the method can enable detection of the target symbol sequencewith higher accuracy and less computational resource.

According to a seventh aspect of the present invention, optionallyprovided is the method of the sixth aspect of the sixth aspect, wherethe estimating whether each candidate is the target symbol sequence,based on the repetition indication of each corresponding candidatefurther includes determining which candidate outputs the highestprobability from the recurrent neural network among the candidates.According to the seventh aspect, the method can enable detection of thetarget symbol sequence with higher accuracy and less computationalresource.

According to an eighth aspect of the present invention, optionallyprovided is the method of the sixth aspect, where the extracting arelated portion for each candidate from the speech-to-text data includesextracting a plurality of the related portions of the candidates fromthe speech-to-text data, where the estimating a probability that eachcandidate is the target symbol sequence by inputting the related portionof each of the candidates with labelled repetition into a recurrentneural network includes inputting each of the plurality of the relatedportions of the each of the candidates with labelled repetition intoeach of a plurality of recurrent neural networks, and where each of theplurality of the related portions of each of the candidates withrepetition indications is input to each of the plurality of recurrentneural networks in a direction depending on a location of each of theplurality of the related portions to the each of the candidates.According to the eighth aspect, the method can enable detection of thetarget symbol sequence with higher accuracy and less computationalresource, by utilizing relative locations between the candidates and therelated portions in the speech-to-text data.

According to a ninth aspect of the present invention, optionallyprovided is the method further including requiring additionalspeech-to-text data in response to determining that the probabilitiesfor the candidates are below a threshold. According to the ninth aspect,the method can enable another estimation of the target symbol sequencefrom a new speech-to-text data if the estimation from existingspeech-to-text data is not considered to be reliable enough.

According to a tenth aspect of the present invention, optionallyprovided is the method of the first aspect, where the labeling thedetected repetition with the repetition indication includes replacingthe detected repetition with the repetition indication. According to thetenth aspect, the method can enable detection of the target symbolsequence with higher accuracy and less computational resources bydeleting unnecessary information.

According to an eleventh aspect of the present invention, optionallyprovided is the method of the first aspect, where the labeling thedetected repetition with the repetition indication includes labeling thedetected repetition with an indication of a symbol length of thedetected repetition. According to the eleventh aspect, the method canenable detection of the target symbol sequence with higher accuracy andless computational resources by utilizing information of a symbol lengthof the repetition.

According to an twelfth aspect of the present invention, optionallyprovided is the method of the first aspect, where the labeling thedetected repetition with the repetition indication includes labeling thedetected repetition with an indication of a location of the detectedrepetition in the each candidate. According to the twelfth aspect, themethod can enable detection of the target symbol sequence with higheraccuracy and less computational resources by utilizing information of alocation of repetition.

According to a thirteenth aspect of the present invention, optionallyprovided is the method of the first aspect, further including detectinga similar portion that is similar to at least a partial sequence of eachof the candidates from the related portion of each of the candidates,and labeling the detected similar portion with information indicatingsimilarity, and where the estimating whether each candidate is thetarget symbol sequence, using the corresponding related portionincluding the repetition indication of each of the candidates includesestimating whether each of the candidates is the target symbol sequence,based on the repetition indication and similar portions of eachcandidate. According to the thirteenth aspect, the method can enabledetection of the target symbol sequence with higher accuracy and lesscomputational resources by utilizing information of portions that aresimilar to the candidates.

The first-thirteenth aspects above can also include apparatus thatperforms the described methods and computer program product causing acomputer or programmable circuitry to perform the described methods. Thesummary clause does not necessarily describe all features of theembodiments of the present invention. Embodiments of the presentinvention can also include sub-combinations of the features describedabove.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The following description will provide details of preferred embodimentswith reference to the following figures wherein:

FIG. 1 shows an exemplary configuration of an apparatus, according to anembodiment of the present invention;

FIG. 2 shows an operational flow according to an embodiment of thepresent invention;

FIG. 3 shows candidates according to an embodiment of the presentinvention;

FIG. 4 shows candidates according to another embodiment of the presentinvention;

FIG. 5 shows related portions according to an embodiment of the presentinvention;

FIG. 6 shows labeling, according to an embodiment of the presentinvention;

FIG. 7 shows labeling, according to another embodiment of the presentinvention;

FIG. 8 shows a Recurrent Neural Network (RNN) according to an embodimentof the present invention;

FIG. 9 shows a Long Short-Term Memory (LSTM) according to an embodimentof the present invention;

FIG. 10 shows an estimation model according to an embodiment of thepresent invention;

FIG. 11 shows a second operational flow according to an embodiment ofthe present invention; and

FIG. 12 shows an exemplary hardware configuration of a computer thatfunctions as a system, according to an embodiment of the presentinvention.

DETAILED DESCRIPTION

Hereinafter, example embodiments of the present invention will bedescribed. The example embodiments shall not limit the inventionaccording to the claims, and the combinations of the features describedin the embodiments are not necessarily essential to the invention.

FIG. 1 shows an exemplary configuration of an apparatus 10 (e.g., acomputer, programmable circuitry, etc.), according to an embodiment ofthe present invention. The apparatus 10 can determine a target symbolsequence in speech-to-text data. The target symbol sequence determinedby the apparatus 10 may be a phone number.

The apparatus 10 can comprise a processor and one or more computerreadable mediums collectively including instructions. The instructions,when executed by the processor or programmable circuitry, can cause theprocessor or the programmable circuitry to operate as a plurality ofoperating sections. Thereby, the apparatus 10 can be represented as astoring section 100, an obtaining section 110, a detecting section 130,an extracting section 140, a searching section 150, a labeling section160, an estimating section 170, and a training section 190.

The storing section 100 can store a variety of data used for operationsof the apparatus 10. The storing section 100 can comprise a volatile ornon-volatile memory. One or more other elements in the apparatus 10(e.g., the obtaining section 110, the detecting section 130, theextracting section 140, the searching section 150, the labeling section160, the estimating section 170, the training section 190, etc.) cancommunicate necessary data directly or via the storing section 100.

The obtaining section 110 can obtain speech-to-text data. The obtainingsection 110 can obtain one or more training data, each training dataincluding a speech-to-text data paired with a correct symbol sequence.The obtaining section 110 can obtain the speech-to-text data and/or thetraining data from a database 20, and can store them in the storingsection 100. The obtaining section 110 can obtain the speech-to-textdata and/or the training data from a microphone or other audio inputdevice connected to the apparatus. The speech-to-text data and/or thetraining data can be captured human speech or mechanically synthesizedhuman speech.

The detecting section 130 can detect one or more candidates of a targetsymbol sequence from the speech-to-text data obtained by the obtainingsection 110. The detecting section 130 can perform the detecting on theone or more candidates by extracting two or more symbol sequences thatconstitute each of the candidates from the speech-to-text data. The twoor more symbol sequences are separate from each other in thespeech-to-text data.

The extracting section 140 can extract one or more related portions ofeach candidate detected by the detecting section 130, from thespeech-to-text data. In some embodiments, the related portion(s) may betext adjacent to each candidate in the speech-to-text data.

The searching section 150 can search repetition in the related portionextracted by the extracting section 140. The searching section 150 candetect repetition of at least a partial sequence of each candidatewithin the related portion of the corresponding candidate.

The labeling section 160 can label the detected repetition detected bythe searching section 150, with a repetition indication.

The estimating section 170 can estimate whether each candidate is thetarget symbol sequence, using the corresponding related portionincluding the repetition indication of each of the candidates labeled bythe labeling section 160. In some embodiments, the estimating section170 can estimate a possibility of whether each candidate is the targetsymbol sequence by utilizing an estimation model such as a recurrentneural network.

The training section 190 can train the estimation model used for theestimation by the estimating section 170. The training section 190 canperform the training by using the training data obtained by theobtaining section 110.

FIG. 2 shows a first operational flow according to an embodiment of thepresent invention. The present embodiment describes an example in whichan apparatus, such as the apparatus 10, performs the operations fromS110 to S190, as shown in FIG. 2. The apparatus can estimate a targetsymbol sequence from speech-to-text data by performing the operations ofS110-S190.

The target symbol sequence can be a sequence of symbols including, e.g.,numbers, letters, and/or other characters, and may be meaningless byitself. In some embodiments, the target symbol sequence may be a phonenumber, a customer ID, a card number, an identification of person/groupof people, an identification of product/service, and physical/emailaddress, etc.

At S110, an obtaining section, such as the obtaining section 110, canobtain speech-to-text data. In other embodiments, the obtaining section110 can obtain text data transcribed from a verbal conversation ormonolog, or text data of a text message (e.g., online chat), as thespeech-to-text data.

At S130, a detecting section such as the detecting section 130 candetect one or more candidates (which can hereinafter be referred to as“candidates”) of a target symbol sequence from the speech-to-text data.The detecting section can detect candidates that have the same number ofsymbols as the target symbol sequence. When the target symbol sequenceis a phone number having 11 symbol sequences (or regarded as 11-digits),the detecting section can detect one or more candidates having 11 symbolsequences from the speech-to-text data.

The detecting section can extract two or more symbol sequences thatconstitute each of the candidates, from the speech-to-text data, suchthat the two or more symbol sequences are separate from each other inthe speech-to-text data. The detecting section can apply one or moretemplates that extract a predetermined number of symbol sequences fromthe speech-to-text data, to the speech-to-text data. The concatenationof the two or more symbol sequences forms each of the candidates.

FIG. 3 shows candidates according to an embodiment of the presentinvention. In the embodiment of FIG. 3, the target symbol sequence is an11-digit phone number. The “Candidates” shown in the table (e.g.,08008012551, 08008012513 . . . ) represents the candidates of the targetsymbol sequence detected by the detecting section. The detecting sectiondetects candidates from the speech-to-text data “My phone number is . .. hmm 5131 right” as shown in the top of FIG. 3.

In the embodiment of FIG. 3, the detecting section detects thecandidates by utilizing an 8-digits template and a 3-digits template. Asto the embodiment of FIG. 3, the detection section can extract allsymbol sequences having 8-digits (e.g., 08008012, 80080123 . . . ) fromthe speech-to-text data by using the 8-digits template. The detectingsection can extract all symbol sequences having 3-digits (e.g., 551, 513. . . ) from the speech-to-text data by using the 3-digits template suchthat 8-digits symbol sequences do not overlap 3-digits symbol sequencesat the same time. For example, when detecting “08008012”, the detectingsection cannot detect “080”, “800”, . . . , “234” from thespeech-to-text data as 3-digit symbol sequences. The concatenation ofthe symbol sequences (e.g., 08008012) and (e.g., 551) forms thecandidate (e.g., 08008012551).

The detecting section can detect the same symbol sequence that isextracted from different portions in the speech-to-text data, as two ormore candidates. In the embodiment of FIG. 3, the detecting sectiondetects “08008012513” as shown in the second candidate and the fourthcandidate. For example, the detecting section detects “08008012” fromthe same portion in the speech-to-text data for the candidates, whilethe detecting section detects “513” of the second candidate from “ . . .is 55131 hmm . . . ” in the speech-to-text data and detects “513” of thefourth candidate from “ . . . hmm 5131 right” in the speech-to-textdata.

In such a case, the detecting section can treat the two candidates of“08008012513” as different candidates. In alternative embodiments, thedetection section can maintain some of a plurality of candidates havingthe same symbol sequence while abandoning the other candidates.

FIG. 4 shows candidates according to another embodiment of the presentinvention. In the embodiment of FIG. 4, the detecting section detectsthe candidates by utilizing a 3-digit template and two 4-digittemplates. As to the embodiment in FIG. 4, the detection section canextract all symbol sequences having 3 digits (e.g., 080, 800, 008 . . .) from the speech-to-text data by using the 3-digit template. Thedetecting section can extract all symbol sequences having 4 digits(e.g., 0801, 8012 . . . ) from the speech-to-text data by using thefirst 4-digit template such that 3-digit symbol sequences do not overlapany 4-digit symbol sequences at the same time. The detecting section canalso extract all symbol sequences having 4 digits (e.g., 5513, 5131 . .. ) from the speech-to-text data by using the second 4-digit templatesuch that 3-digit symbol sequences and 4-digit symbol sequencesextracted by the first 4-digit template do not overlap any 4-digitsymbol sequences extracted by the second 4-digit template at the sametime.

In some embodiments, the detecting section can use all possiblecombinations of templates for detecting the symbol sequences. Forexample, the detecting section can use an 11-digit template, 10&1-digittemplates, 9&2-digit templates, . . . , 1&10-digit templates,9&1&1-digit templates, . . . , 1&1&9-digit templates, 8&1&1&1-digittemplates, . . . , 1&1&1&8-digit templates, . . . , and1&1&1&1&1&1&1&1&1&1&1-digit templates, for the target symbol having an11-digit phone number. In an embodiment, the detecting section can useonly some of all possible combinations of templates for detecting thesymbol sequences, which can be predetermined by a user of the apparatus.

As explained in relation to the foregoing embodiments, the detectingsection can perform the detection such that the two or more symbolsequences extracted by the templates do not overlap. In alternativeembodiments, the two or more symbol sequences extracted by templates canoverlap.

At S140, an extracting section, such as the extracting section 140, canextract a related portion of each candidate detected at S130, from thespeech-to-text data. The related portion of each of the candidatesincludes a portion directly or indirectly adjacent to any of the two ormore symbol sequences constituting the candidates.

In an embodiment, the extracting section can extract a plurality of therelated portions of the candidates from the speech-to-text data. Theextracting section can extract at least one of a preceding portion (orleft words) of the first symbol sequences extracted at S130, subsequentportion (or right words) of the last symbol sequences extracted at S130,and a sandwiched portion (or middle words) between two adjacent symbolsequences extracted at S130.

In some embodiments, the extracting section can extract a designatednumber of words (e.g., 10 words) or characters (e.g., 100 characters) asthe related portion from the speech-to-text data. In an alternativeembodiment, the extracting section can extract all words between thebeginning of the speech-to-text and the first extracted symbolsequences, all words between the end of the speech-to-text and the lastextracted symbol sequences, and/or all words between the two adjacentsymbol sequences as the related portions.

FIG. 5 shows related portions according to an embodiment of the presentinvention. FIG. 5 shows the candidates detected according to theembodiments of FIG. 3. In the embodiment of FIG. 5, the extractingsection has extracted “My phone number is,” which precedes the firstsymbol sequence “08008012” in the speech-to-text data as the firstrelated portion (shown as “Left Words”). The extracting section alsoextracts “31 hmm 5131 right” that is subsequent to the second symbolsequence “551” in the speech-to-text data as the second related portion(shown as “Right Words”).

At S150, a searching section, such as the searching section 150, candetect repetition of at least a partial sequence of each candidatewithin the related portion of the corresponding candidate. The searchingsection can detect at least one of the two or more symbol sequences thatconstitute the corresponding candidate, within the related portion ofthe corresponding candidates, as the repetition. In some embodiments,the searching section can detect symbol sequences that are the same asthe symbol sequence(s) detected at S130 as a part of a candidate in arelated portion adjacent to the detected symbol sequence(s).

In alternative embodiments, the searching section can detect symbolsequences that are the same as the symbol sequence(s) detected at S130as a part of one candidate in a part of/all related portions of thecandidate. In further alternative embodiments, the searching section candetect symbol sequences that are the same as any portion of onecandidate in all related portions of the one candidate. When thesearching section does not detect repetition for a candidate, theapparatus can proceed with an operation of S170 without performing S160for the candidate.

At S160, a labeling section, such as the labeling section 160, can labelthe repetition detected at S150 with a repetition indication. Thelabeling section can perform labeling by replacing the detectedrepetition with the repetition indication.

In some embodiments, the labeling section can label the detectedrepetition with an indication of a symbol length of the detectedrepetition. For example, the repetition indication may includeinformation of the number of symbols of the detected repetition.

In other embodiments, the labeling section can label the detectedrepetition with an indication of a location of the detected repetitionin the corresponding candidate. For example, the repetition indicationmay include information of a location in which the related portionincludes the detected repetition (e.g., information that the detectedrepetition exists in the last 4-digits in the related portion).

FIG. 6 shows labeling, according to an embodiment of the presentinvention. FIG. 6 shows repetition indications given to the relatedportions detected according to the embodiments of FIG. 5. In theembodiment of FIG. 6, the searching section has detected repetition ofthe 3-digit symbol sequence “513” under Right Words in the secondcandidate in FIG. 5, and in response the labeling section has replacedthe repetition “513” with the repetition indication “Rep(3).” “(3)” in“Rep(3)” represents the number of symbols within the repetition as shownin FIG. 6. The searching section also detects repetition of the 3-digitsymbol sequence “131” under Right Words in the third candidate in FIG.5, and the labeling section replaces the repetition “131” with therepetition indication “Rep(3).”

In response to two or more repetition indications being in a candidateor a related portion, the labeling section can label the detectedrepetitions with distinct repetition indications. For example, if thereare two “Rep(3)” indications, the labeling section may label the first“Rep(3)” as “Rep(3)_1” and the second “Rep(3)” as “Rep(3)_2.”

FIG. 7 shows labeling, according to another embodiment of the presentinvention. In the embodiment of FIG. 7, the related portions includeleft words, middle words, and right words that are extracted from thecandidates and the speech-to-text data shown in FIG. 3. The middle wordscan be a portion that is sandwiched between 8-digit symbol sequences and3-digit symbol sequences detected by the 8-digit template and the3-digit template, respectively. In the embodiment, the repetition underRight Words of the second and the third candidate is replaced with therepetition indication.

In the embodiment of FIG. 7, the searching section can detect arepetition of both of 8-digit symbol sequences (e.g., “08008012”) and3-digit symbol sequences (e.g., “551”) from the middle words (e.g., “34oh . . . is”). The labeling section can label the repetition such thatthe repetitions of different symbol sequences (3-digits/8-digits symbolsequences) are distinguishable.

At S170, an estimating section, such as the estimating section 170, canestimate whether each candidate is the target symbol sequence. In someembodiments, the estimating section can calculate a probability thateach candidate is the target symbol sequence by inputting the relatedportion of each candidate with the repetition indication into anestimation model. The estimating section can use a recurrent neuralnetwork such as Long Short-Term Memory (LSTM) as the estimation model.The estimating section may adopt at least one of a variety of types ofLSTM (e.g., LSTM disclosed in Gers & Schmidhuber (2000), Cho, et al.(2014), Koutnik, et al. (2014), Yao et al. (2015), Greff, et al. (2015),or Jozefowicz, et al (2015)). The estimating section may adopt GRU as atype of LSTM, as disclosed by Junyoung Chung, Caglar Gulcehre, KyungHyunCho, Yoshua Bengio, Empirical Evaluation of Gated Recurrent NeuralNetworks on Sequence Modeling. In an alternative embodiment, theestimating section can adopt another type of recurrent neural network asthe estimation model.

The estimating section can input all or at least part of the relatedportions with the repetition indication into the LSTM. When thesearching section does not detect any repetition in the related portionat S150, the estimating section can input the related portion withoutany repetition indications into the LSTM.

At S170, the estimating section can list the plurality of candidates inascending/descending order of the possibility of being the target symbolsequence, and can show the list of the candidates and the possibilitiesthereof on a display of the apparatus.

The apparatus can perform operations S140-S170 for each of a pluralityof candidates that the detecting section has detected at S130. If theapparatus uses two or more templates (e.g., 8&3-digit templates and4&4&3-digit templates), then the apparatus can perform operationsS140-S170 for each of the plurality of candidates detected from alltemplates.

Thereby, the estimating section can calculate the possibility of thetarget symbol sequence for each of the plurality of candidates detectedat S130.

At S190, the estimating section can select one candidate as the targetsymbol sequence among the plurality of candidates. In an embodiment, theestimating section can determine which candidate outputs the highestprobability from the recurrent neural network among the plurality ofcandidates. The estimating section can select the candidate determinedto output the highest probability as the target symbol sequence.

The obtaining section can employ additional speech-to-text data inresponse to the estimating section determining that the probability foreach candidate is below a threshold. The obtaining section can utilizeadditional speech-to-text data if the highest probability of theplurality of candidates is below a threshold. The obtaining section canmake use of additional speech-to-text data if a difference between thehighest probability of the plurality of candidates and the secondhighest probability of the plurality of candidates is below a threshold.Thereby, for example, an operator at a call center can use the apparatusfor inputting information of a customer, and can again request a symbolsequence (e.g., customer ID) from the customer, in response to anapparatus not being confident with the estimated symbol sequence.

As explained above, the apparatus can accurately detect the targetsymbol sequence with less computational resources by utilizing theindication of repetition. In particular, the apparatus can distinguishthe target symbol sequence (e.g., phone number) from other confusingsymbol sequences (e.g., product ID) within speech-to-text data with lesscomputational resources by utilizing the indication of repetition.

Speakers sometimes repeat at least part of a symbol sequence that isimportant in conversation for confirmation. The apparatus can utilizesuch repetition for identifying the target symbol sequence.Specifically, during a conversation between an agent and a client, theagent can wholly or partially confirm information of the client (e.g.,customer ID, phone number, etc.). Therefore, the apparatus can use onlythe portion of speech-to-text data corresponding to the agent's speechamong whole conversation between the agent and the client, and determinethe target symbol sequence based on that portion of the speech-to-textdata.

FIG. 8 shows a Recurrent Neural Network (RNN) according to an embodimentof the present invention. In one embodiment, the RNN comprises ahardware implementation. In another embodiment, the RNN comprises arecurrent layer 210 and an output layer 220. As shown in FIG. 2, therecurrent layer 210 can iteratively receive a new input and calculate anext state based on the new input and a current state for each timepoint. In other words, the recurrent layer 210 can update a state foreach input.

The recurrent layer 210 can provide the output layer 220 with an outputof the recurrent layer 210 (e.g., the last state) for the candidatedata. The recurrent layer 210 according to the embodiment can beimplemented by an estimating section, such as the estimating section170, and/or a training section, such as the training section 190.

The output layer 220 can process a resultant output data based on theoutput from the recurrent layer 210. The output layer 220 can be asoftmax layer or a hierarchical softmax layer. The output layer 220 canbe implemented by the estimating section and/or the training section.

FIG. 9 shows an LSTM according to an embodiment of the presentinvention. For example, a recurrent layer in the RNN such as therecurrent layer 210 can be implemented by the LSTM represented in FIG.9. In such embodiments, a state (referred to as “the current state” and“the next state” above) includes a hidden state h_(t) and a cell statec_(t) for a time point t, where t=1, . . . , T.

In the embodiment of FIG. 9, the LSTM can input (c₀, h₀, x₁), calculate(c₁, h₁), and output y₁ at a time point 1, . . . , input (c_(t−1),h_(t−1), x_(t)) and calculate (c_(t), h_(t)) at a time point t, input(c_(t), h_(t), x_(t+1)) and calculate (c_(t+1), h_(t+1)) at a time pointt+1, . . . , input (c_(T−1), h_(T−1), x_(T)) and calculate (c_(T),h_(T)) at a time point T. The LSTM can output yt for time point t, whichcan be the same as the hidden state h_(t). The LSTM can output y_(T) atthe last time point T as the last state of the recurrent layer.

FIG. 10 shows an estimation model according to an embodiment of thepresent invention. The estimating section can use a plurality ofrecurrent neural networks for processing a candidate. In an embodiment,the estimating section can input each of a plurality of the relatedportions of each candidate with labeled repetitions into one of aplurality of recurrent neural networks, each having independent weights.The estimating section can input each word (or the repetitionindication) in a related portion into a recurrent neural network in adirection of an order of the text (i.e., left to right) or a directionof an inverse order of the text (i.e., right to left).

Each of the plurality of the related portions of each candidate withrepetition indications is input to one of the plurality of recurrentneural networks in a direction depending on a location of each of theplurality of the related portions of each candidate or the symbolsequences constituting each candidate. Thereby, the estimating sectioncan reduce computational resources and achieve high accuracy ofestimation of the target symbol sequence by taking relative location ofthe related portion and the candidate/symbol sequences intoconsideration.

In the embodiment of FIG. 10, the speech-to-text data is the same asFIG. 3, and the candidate is “08008012513” comprised of symbol sequence“08008012” detected by the 8-digit template and symbol sequence “513”detected by the 3-digit template. The related portion includes leftwords “My phone number is”, middle words “34 oh cool it's easy toremember yeah and the number is 5” and right words “1 hmm rep(3) 1right” having the repetition indication “rep(3).”

In the embodiment of FIG. 10, the estimating section can use LSTM1 forthe left words, use LSTM2 and LSTM3 for the middle words, and use LSTM4for the right words. The estimating section can input the left wordsinto LSTM1 in an original order of the left words. For example, theestimating section can first input a first word “My” of the left wordsinto LSTM1 and calculate a first output of the first word, then inputthe first output and the second word “phone” into LSTM1 and calculate asecond output, then input the second output and the third word “number”into LSTM1 and calculate a third output, input the third output and thefourth word “is” into LSTM1 and calculate a fourth output, and input thefourth output (i.e., the last output) into a softmax layer.

The estimating section can input the middle words into LSTM2 in anoriginal order of the middle words. The estimating section can firstinput a first word “3” of the middle words into LSTM2 and calculate afirst output of the first word, then input the first output and thesecond word “4” into LSTM2 and calculate a second output, . . . , inputthe thirteenth output and a fourteenth word “5” into LSTM2 and calculatea fourteenth output (i.e., the last output), and input the fourteenthoutput into the softmax layer.

The estimating section can also input the middle words into LSTM3 in aninverse order of the middle words. The estimating section can firstinput a first word “5” into LSTM3 and calculate a first output of thefirst word, then input the first output and the second word “is” intoLSTM3 and calculate a second output, . . . , input the thirteenth outputand a fourteenth word “3” into LSTM3 and calculate a fourteenth output(i.e., the last output), and input the fourteenth output into thesoftmax layer. Thereby the estimating section may input the relatedportion between the two symbol sequences into bi-directional LSTMs.

The estimating section can also input right words into LSTM4 in theinverse order of the right words. The estimating section can first inputa first word “right” into LSTM4 and calculate a first output of thefirst word, then input the first output and the second word “1” intoLSTM4 and calculate a second output, then input the second output andthe third word (or the repetition indication) “rep(3)” into LSTM4 andcalculate a third output, then input the third output and the fourthword “hmm” into LSTM4 and calculate a fourth output, then input thefourth output and a fifth word “1” into LSTM4 and calculate a fifthoutput (i.e., the last output), and input the fifth output into thesoftmax layer.

The estimating section can estimate the possibility of the target symbolsequence by performing calculation of the softmax layer, based on theoutputs received from LSTM1, LSTM2, LSTM3, and LSTM4. Thereby, accordingto the embodiment of FIG. 10, the estimating section can maintainaccuracy of estimation of the target symbol sequence with lesscomputational resources than an embodiment where only one LSTM is usedas the estimation model.

FIG. 11 shows a second operational flow according to an embodiment ofthe present invention. The present embodiment describes an example inwhich an apparatus, such as the apparatus 10, performs the operationsfrom S310 to S350, as shown in FIG. 11. The apparatus can train arecurrent neural network to estimate a target symbol sequence byperforming the operation of S310-S350.

At S310, an obtaining section, such as the obtaining section 110, canobtain one or more training data for training an estimation model suchas a recurrent neural network. Each training data can include aspeech-to-text data paired with a symbol sequence used as the correctsymbol sequence. The correct symbol sequence can be previouslydetermined by a person who reviews the speech-to-text data.

At S330, the apparatus can process the training data obtained at S310 toextract related portions with repetition indications for eachspeech-to-text data of the two or more of training data. In someembodiments, the apparatus can perform the operations of S110-S160 foreach speech-to-text data of the two or more training data.

At S350, a training section such as the training section 190 can train arecurrent neural network such as LSTM explained in relation to S170 bybackpropagation. In such embodiments, the training section can performthe training by updating weights (or parameter) of the LSTM so as toreduce errors between the allocated probability and the output of theLSTM of candidates of each speech-to-text data. In the embodiment, thetraining section can allocate a probability of 1 (or 100%) tocandidate(s) that is the same as the correct symbol sequence, and,allocating a probability of 0 (or 0%) to other candidate(s).

The training section can iterate updating the weights of each LSTM untila sum of errors obtained from a plurality of candidates of two or morespeech-to-text data is below a threshold, or does not reduce by athreshold.

In the embodiment of FIG. 10, the training section can train four LSTMs(LSTMs 1-4) and the softmax layer. Thereby, the apparatus can optimizeLSTMs to detect the target symbol sequence in both direction of thetext.

In many embodiments, the apparatus can detect the same portion as a partof a candidate as a repetition. In alternative embodiments, thesearching section can detect a similar portion that is similar to atleast a partial sequence of each of the candidates from the relatedportion of each of the candidates. The similar portion can be differentin one or two symbols from the at least partial sequence (e.g., symbolsequences detected by templates) of each candidate. The labeling sectioncan label the detected similar portion with information indicatingsimilarity (e.g., “SIM(3)”). The estimating section can estimate whethereach of the candidates is the target symbol sequence, based on theindicated repetition and the similar portion of the each candidate.

In some embodiments, the apparatus can determine one or more templatesused at S130 of FIG. 2 based on a result of the training. For example,the apparatus can perform the operations of FIG. 11 for each of thepossible templates by one part of the training data to generate aplurality of estimation models corresponding to each possible template.The apparatus can evaluate each estimation model by the other part ofthe training data, and select a part of the possible templates based ona result of the evaluation.

Although many embodiments utilizing the recurrent neural network areexplained above, in some embodiments the apparatus can use SupportVector Machine (SVM) instead of the recurrent neural network as theestimation model. In the embodiments, the estimating section can inputBag of Words instead of text itself into the estimation model as therelated portions. For example, at the operation of S170 in FIG. 2, theestimating section can generate Bag of Words corresponding to therelated portion(s) with the repetition indication generated at S160,then input the Bag of Words into SVM. At the operation of S350, thetraining section can train SVM instead of the recurrent neural networkby utilizing Bag of Words generated from the training data. Inalterative embodiments, the training section can train any otherdiscriminative models as the estimation model by utilizing Bag or Words.According to these embodiments, the related portions are represented bya fixed length vectors.

FIG. 12 shows an exemplary hardware configuration of a computerconfigured for cloud service utilization, according to an embodiment ofthe present invention. A program that is installed in the computer 800can cause the computer 800 to function as or perform operationsassociated with apparatuses of the embodiments of the present inventionor one or more sections (including modules, components, elements, etc.)thereof, and/or cause the computer 800 to perform processes of theembodiments of the present invention or steps thereof. Such a programcan be executed by the CPU 800-12 to cause the computer 800 to performcertain operations associated with some or all of the blocks offlowcharts and block diagrams described herein.

The computer 800 according to the present embodiment includes a CPU800-12, a RAM 800-14, a graphics controller 800-16, a sound controller,and a display device 800-18, which are mutually connected by a hostcontroller 800-10. The computer 800 also includes input/output unitssuch as a communication interface 800-22, a hard disk drive 800-24, aDVD-ROM drive 800-26 and an IC card drive, which are connected to thehost controller 800-10 via an input/output controller 800-20. Thecomputer also includes legacy input/output units such as a ROM 800-30and a keyboard 800-42, which are connected to the input/outputcontroller 800-20 through an input/output chip 800-40.

The CPU 800-12 operates according to programs stored in the ROM 800-30and the RAM 800-14, thereby controlling each unit. The graphicscontroller 800-16 obtains image data generated by the CPU 800-12 on aframe buffer or the like provided in the RAM 800-14 or in itself, andcauses the image data to be displayed on the display device 800-18. Thesound controller can obtain sound from a connected microphone or otheraudio input device. The sound controller can generate sound on aconnected speaker or other audio output device.

The communication interface 800-22 communicates with other electronicdevices via a network 800-50. The hard disk drive 800-24 stores programsand data used by the CPU 800-12 within the computer 800. The DVD-ROMdrive 800-26 reads the programs or the data from the DVD-ROM 800-01, andprovides the hard disk drive 800-24 with the programs or the data viathe RAM 800-14. The IC card drive reads programs and data from an ICcard, and/or writes programs and data into the IC card.

The ROM 800-30 stores therein a boot program or the like executed by thecomputer 800 at the time of activation, and/or a program depending onthe hardware of the computer 800. The input/output chip 800-40 can alsoconnect various input/output units via a parallel port, a serial port, akeyboard port, a mouse port, and the like to the input/output controller800-20.

A program is provided by a computer program product (e.g., computerreadable media such as the DVD-ROM 800-01 or the IC card). The programis read from the computer readable media, installed into the hard diskdrive 800-24, RAM 800-14, or ROM 800-30, which are also examples ofcomputer readable media, and executed by the CPU 800-12. The informationprocessing described in these programs is read into the computer 800,resulting in cooperation between a program and the above-mentionedvarious types of hardware resources. An apparatus or method can beconstituted by realizing the operation or processing of information inaccordance with the usage of the computer 800.

For example, when communication is performed between the computer 800and an external device, the CPU 800-12 can execute a communicationprogram loaded onto the RAM 800-14 to instruct communication processingto the communication interface 800-22, based on the processing describedin the communication program. The communication interface 800-22, undercontrol of the CPU 800-12, reads transmission data stored on atransmission buffering region provided in a recording medium such as theRAM 800-14, the hard disk drive 800-24, the DVD-ROM 800-01, or the ICcard, and transmits the read transmission data to network 800-50 orwrites reception data received from network 800-50 to a receptionbuffering region or the like provided on the recording medium.

In addition, the CPU 800-12 can cause all or a necessary portion of afile or a database to be read into the RAM 800-14, the file or thedatabase having been stored in an external recording medium such as thehard disk drive 800-24, the DVD-ROM drive 800-26 (DVD-ROM 800-01), theIC card, etc., and perform various types of processing on the data onthe RAM 800-14. The CPU 800-12 can then write back the processed data tothe external recording medium.

Various types of information, such as various types of programs, data,tables, and databases, can be stored in the recording medium to undergoinformation processing. The CPU 800-12 can perform various types ofprocessing on the data read from the RAM 800-14, which includes varioustypes of operations, processing of information, condition judging,conditional branch, unconditional branch, search/replace of information,etc., as described throughout this disclosure and designated by aninstruction sequence of programs, and writes the result back to the RAM800-14.

In addition, the CPU 800-12 can search for information in a file, adatabase, etc., in the recording medium. For example, when a pluralityof entries, each having an attribute value of a first attribute isassociated with an attribute value of a second attribute, are stored inthe recording medium, the CPU 800-12 can search for an entry matchingthe condition whose attribute value of the first attribute isdesignated, from among the plurality of entries, and reads the attributevalue of the second attribute stored in the entry, thereby obtaining theattribute value of the second attribute associated with the firstattribute satisfying the predetermined condition.

The above-explained program or software modules can be stored in thecomputer readable media on or near the computer 800. In addition, arecording medium such as a hard disk or a RAM provided in a serversystem connected to a dedicated communication network or the Internetcan be used as the computer readable media, thereby providing theprogram to the computer 800 via the network.

The present invention can be a system, a method, and/or a computerprogram product. The computer program product can include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions can execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer can be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection can be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) can execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to individualize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block can occur out of theorder noted in the figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the embodiments of the present invention have been described, thetechnical scope of the invention is not limited to the above describedembodiments. It is apparent to persons skilled in the art that variousalterations and improvements can be added to the above-describedembodiments. It is also apparent from the scope of the claims that theembodiments added with such alterations or improvements can be includedin the technical scope of the invention.

The operations, procedures, steps, and stages of each process performedby an apparatus, system, program, and method shown in the claims,embodiments, or diagrams can be performed in any order as long as theorder is not indicated by “prior to,” “before,” or the like and as longas the output from a previous process is not used in a later process.Even if the process flow is described using phrases such as “first” or“next” in the claims, embodiments, or diagrams, it does not necessarilymean that the process must be performed in this order.

As made clear from the above, the embodiments of the present inventionenable a learning apparatus learning a model corresponding totime-series input data to have higher expressive ability and learningability and to perform the learning operation more simply.

What is claimed is:
 1. A computer-implemented method comprising:detecting repetitions of at least a partial sequence of related portionsof one or more candidates of a target symbol sequence from aspeech-to-text data; labeling the detected repetitions with a repetitionindication; and estimating, by employing a trained estimation model witha plurality of Long Short-Term Memory (LSTM) neural networks, whethereach candidate is the target symbol sequence, using the correspondingrelated portion including the repetition indication of each of thecandidates, wherein each candidate is divided into a plurality of wordgroups, with each of the plurality of word groups being fed into one ofa plurality of LSTM neural networks to generate a plurality of outputs,with the plurality of outputs being fed into a softmax layer.
 2. Themethod of claim 1, wherein the detecting the one or more candidates ofthe target symbol sequence from the speech-to-text data includesextracting two or more symbol sequences that constitute each of thecandidates, from the speech-to-text data, wherein the two or more symbolsequences are separate from each other in the speech-to-text data. 3.The method of claim 2, wherein detecting repetition of at least apartial sequence of each candidate within the related portion of thecorresponding candidate includes detecting at least one of the two ormore symbol sequences that constitute the corresponding candidate withinthe related portion of the corresponding candidates.
 4. The method ofclaim 2, wherein the extracting two or more symbol sequences areperformed by extracting the predetermined number of symbol sequences,the two or more symbol sequences do not overlap, and the concatenationof the two or more symbol sequences forms each of the candidates.
 5. Themethod of claim 1, wherein the related portion of each of the candidatesincludes a portion adjacent to the each of the candidates.
 6. The methodof claim 5, wherein the estimating whether each candidate is the targetsymbol sequence, based on the repetition indication of eachcorresponding candidate includes estimating a probability that eachcandidate is the target symbol sequence by inputting the related portionof each candidate with the repetition indication into a recurrent neuralnetwork.
 7. The method of claim 6, wherein the estimating whether eachcandidate is the target symbol sequence, based on the repetitionindication of each corresponding candidate further includes determiningwhich candidate outputs the highest probability from the recurrentneural network among the candidates.
 8. The method of claim 6, whereinthe extracting a related portion for each candidate from thespeech-to-text data includes extracting a plurality of the relatedportions of the candidates from the speech-to-text data, wherein theestimating a probability that each candidate is the target symbolsequence by inputting the related portion of each of the candidates withlabelled repetition into a recurrent neural network includes inputtingeach of the plurality of the related portions of each of the candidateswith labelled repetition into a recurrent neural network among aplurality of recurrent neural networks, and wherein each of theplurality of the related portions of each of the candidates withrepetition indications is input into a recurrent neural network amongthe plurality of recurrent neural networks in a direction depending on alocation of each of the plurality of the related portions to the each ofthe candidates.
 9. The method of claim 6, further comprising: requiringadditional speech-to-text data in response to determining that theprobabilities for the candidates are below a threshold.
 10. The methodof claim 1, wherein words being recursively fed includes every word ofeach candidate being fed into each respective LSTM neural network togenerate an output and to have each output fed into each respective LSTMneural network along with the word following the word employed togenerate the output.
 11. The method of claim 1, wherein the labeling thedetected repetition with the repetition indication includes labeling thedetected repetition with an indication of a symbol length of thedetected repetition.
 12. The method of claim 1, wherein the labeling thedetected repetition with the repetition indication includes labeling thedetected repetition with an indication of a location of the detectedrepetition in the each candidate.
 13. The method of claim 1, furthercomprising: detecting a similar portion that is similar to at least apartial sequence of each of the candidates from the related portion ofeach of the candidates, and labeling the detected similar portion withinformation indicating similarity, and wherein the estimating whethereach candidate is the target symbol sequence, using the correspondingrelated portion including the repetition indication of each of thecandidates includes estimating whether each of the candidates is thetarget symbol sequence, based on the repetition indication and thesimilar portion of the each candidate.
 14. An apparatus comprising: aprocessor; and one or more computer readable mediums collectivelyincluding instructions that, when executed by the processor, cause theprocessor to perform operations comprising: detecting repetitions of atleast a partial sequence of related portions of one or more candidatesof a target symbol sequence from a speech-to-text data, and labeling thedetected repetitions with a repetition indication, estimating, byemploying a trained estimation model with a plurality of Long Short-TermMemory (LSTM) neural networks, whether each candidate is the targetsymbol sequence, based on the corresponding related portion includingthe repetition indication of each of the candidates, wherein eachcandidate is divided into a plurality of word groups, with each of aplurality of word groups being fed into one of a plurality of LSTMneural networks to generate a plurality of outputs, with the pluralityof outputs being fed into a softmax layer.
 15. The apparatus of claim14, wherein the detecting the one or more candidates of the targetsymbol sequence from the speech-to-text data includes extracting two ormore symbol sequences that constitute each of the candidates, from thespeech-to-text data, wherein the two or more symbol sequences areseparate from each other in the speech-to-text data.
 16. The apparatusof claim 15, wherein detecting repetition of at least a partial sequenceof each candidate within the related portion of the correspondingcandidate includes detecting at least one of the two or more symbolsequences that constitute the corresponding candidate within the relatedportion of the corresponding candidates.
 17. The apparatus of claim 15,wherein the extracting two or more symbol sequences are performed byextracting the predetermined number of symbol sequences, the two or moresymbol sequences do not overlap, and the concatenation of the two ormore symbol sequences forms each of the candidates.
 18. The apparatus ofclaim 17, wherein the related portion of each of the candidates includesa portion adjacent to the each of the candidates.
 19. The apparatus ofclaim 18, wherein the estimating whether each candidate is the targetsymbol sequence, based on the repetition indication of eachcorresponding candidate includes estimating a probability that eachcandidate is the target symbol sequence by inputting the related portionof each candidate with the repetition indication into a recurrent neuralnetwork.
 20. A non-transitory computer readable storage medium havinginstructions embodied therewith, the instructions executable by aprocessor or programmable circuitry to cause the processor orprogrammable circuitry to perform operations comprising: detectingrepetitions of at least a partial sequence of related portions of one ormore candidates of a target symbol sequence from a speech-to-text data,and labeling the detected repetitions with a repetition indication,estimating, by employing a trained estimation model with a plurality ofLong Short-Term Memory (LSTM) neural networks, whether each candidate isthe target symbol sequence, based on the corresponding related portionincluding the repetition indication of each of the candidates, whereineach candidate is divided into a plurality of word groups, with each ofthe plurality of word groups being fed into one of a plurality of LSTMneural networks to generate a plurality of outputs, with the pluralityof outputs being fed into a softmax layer.
 21. The non-transitorycomputer readable storage medium of claim 20, wherein the detecting theone or more candidates of the target symbol sequence from thespeech-to-text data includes extracting two or more symbol sequencesthat constitute each of the candidates, from the speech-to-text data,wherein the two or more symbol sequences are separate from each other inthe speech-to-text data.
 22. The non-transitory computer readablestorage medium of claim 21, wherein detecting repetition of at least apartial sequence of each candidate within the related portion of thecorresponding candidate includes detecting at least one of the two ormore symbol sequences that constitute the corresponding candidate withinthe related portion of the corresponding candidates.
 23. Thenon-transitory computer readable storage medium of claim 21, wherein theextracting two or more symbol sequences are performed by extracting thepredetermined number of symbol sequences, the two or more symbolsequences do not overlap, and the concatenation of the two or moresymbol sequences forms each of the candidates.
 24. The non-transitorycomputer readable storage medium of claim 23 wherein the related portionof each of the candidates includes a portion adjacent to the each of thecandidates.
 25. The non-transitory computer readable storage medium ofclaim 24, wherein the estimating whether each candidate is the targetsymbol sequence, based on the repetition indication of eachcorresponding candidate includes estimating a probability that eachcandidate is the target symbol sequence by inputting the related portionof each candidate with the repetition indication into a recurrent neuralnetwork.